Senior Data Engineer

Kimedics is transforming workforce management in healthcare with a modern platform aimed at improving operational efficiency. The Senior Data Engineer will be responsible for owning the data infrastructure, including pipelines and data quality practices, to support product teams and enhance reporting capabilities.

Responsibilities:

• Own the v1-to-v2 customer data migration: extract, transform, validate, and load with full auditability and a reusable runbook that becomes the pattern for all future migrations
• Build and maintain the core pipeline infrastructure using dbt, Azure Data Factory, and Fabric structured around raw, staging, and conformed data zones in Azure SQL and other Azure data storage
• Establish schema review as part of the product development process, so data consequences are considered before a feature ships, not after
• Define and enforce data modeling standards and naming conventions across product teams
• Build data quality checks and pipeline observability into Azure Monitor alongside existing application alerting
• Partner with the BI developer to maintain clean, tested dbt models that PowerBI reports build on directly
• Maintain lightweight data contracts and documentation for every table used downstream
• Build the data infrastructure that supports AI-powered product features as they ship: embedding pipelines, vector index refresh jobs, and feature tables for ML workloads
• Contribute to the platform by building and supporting data products alongside the broader platform team

Requirements:

• Bachelor's Degree in Computer Science, related technical field, or equivalent practical experience, required
• 4-6 years of professional software engineering experience
• Proven experience independently delivering complex features from design through production deployment
• Experience working with distributed systems, APIs, data pipelines, or cloud infrastructure at production scale
• Experience providing meaningful code review feedback and beginning to mentor junior engineers
• Strong SQL and dbt experience, including building tested, documented models in a production environment
• Hands-on experience with Azure data services: Azure SQL, Azure Data Lake Storage, Azure Data Factory, and Fabric
• Comfort with Python for scripting and pipeline work (not data science, just reliable automation)
• Software engineering practices as defaults: version control, code review, CI/CD for data pipelines, not just for application code
• Experience with schema design and data modeling, not just moving data between systems
• Awareness of how AI features create upstream data requirements: embeddings, vector stores, feature engineering, and the operational difference between data shaped for analytics and data shaped for model training
• Strong communication with product and engineering teams. A significant part of this role is upstream influence
• Snowflake experience is a nice to have